Image analysis algorithms for detection, segmentation, characterization and other forms of information extraction from image data typically rely on the adjustment of specific algorithm parameters, such as threshold levels, noise levels, or the expected size of depicted structures when performing the analysis. For example, for image segmentation, different parameters are set to facilitate identifying regions of interest for use in medical image analysis. In particular, image segmentation may be used to segment structures from background, which may be performed as part of the image analysis, such as for visualization, quantitative image analysis, and image guided intervention.
Image analysis, including image segmentation can be difficult to perform because of the large variability of shape and appearance of different structures. Conventional algorithms that perform image analysis may initially perform image segmentation. However, when image analysis algorithms use rigid models or spatially fixed parameters for performing the image analysis, the results may be inaccurate. For example, ultrasound systems are increasingly used to detect carotid plaque, which may be used to predict cardiovascular disease. Because of the rigid nature of conventional image analysis algorithms and the parameters used, the carotid plaque may not be properly identified, resulting in improper diagnosis or requiring additional scans.